{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SpaCy Tutorial\n",
    "#### 01 Introduction to spaCy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install spaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Install spaCy\n",
    "# !conda install spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download pretrained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SpaCy supports multiple languages, such as English, German, French, Spanish, Portuguese, Italian, Dutch, Greek, Japanese, etc.  \n",
    "\n",
    "Here, we will use a medium-sized pretrained English model. A large model is slower but more accurate while a small model is faster but less accurate and has less features (e.g. no word vectors).\n",
    "\n",
    "The model file contains weights, vocabulary, and model pipeline meta information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Download\n",
    "# !python -m spacy download en_core_web_md\n",
    "# # Restart kernel after the model is downloaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an nlp object\n",
    "nlp = spacy.load(\"en_core_web_md\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example document\n",
    "document = \"\"\"Hey, I'm Yuibi. I work very hard at XYZ, Inc. as a data scientist. \\\n",
    "It's located in San Antonio, TX, which is the best city! 😊 \\\n",
    "You can buy 3 tacos for $2 as of December 13th, 2019. lol\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Created by processing a string of text with the nlp object\n",
    "doc = nlp(document)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenization\n",
    "Tokenization chops sentence(s) into pieces called tokens. For English, it normally uses whitespace as a separator with special treatments for punctuations, emoji. etc. SpaCy allows customization of most of its features (e.g. add infix \"-\" as a seperator for tokenization).  \n",
    "\n",
    "![token](img/token.png)  \n",
    "\n",
    "**Advanced:** Some languages do not use whitespaces to separate words. In those cases, do either\n",
    "1. Segment text into morphemes before tokenization.  \n",
    "2. Use byte-pair-encoding (BPE) to create sub-word units.  \n",
    "BPE is also effective for English as shown in Google's BERT.\n",
    "\n",
    "**Hiererarcy:** Corpus > document > sentence > word > sub-word > character > subcharacter > stroke  \n",
    "Tokens are normally on word, sub-word, or character-level for English."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Hey\n",
      "2 ,\n",
      "3 I\n",
      "4 'm\n",
      "5 Yuibi\n",
      "6 .\n",
      "7 I\n",
      "8 work\n",
      "9 very\n",
      "10 hard\n",
      "11 at\n",
      "12 XYZ\n",
      "13 ,\n",
      "14 Inc.\n",
      "15 as\n",
      "16 a\n",
      "17 data\n",
      "18 scientist\n",
      "19 .\n",
      "20 It\n",
      "21 's\n",
      "22 located\n",
      "23 in\n",
      "24 San\n",
      "25 Antonio\n",
      "26 ,\n",
      "27 TX\n",
      "28 ,\n",
      "29 which\n",
      "30 is\n",
      "31 the\n",
      "32 best\n",
      "33 city\n",
      "34 !\n",
      "35 😊\n",
      "36 You\n",
      "37 can\n",
      "38 buy\n",
      "39 3\n",
      "40 tacos\n",
      "41 for\n",
      "42 $\n",
      "43 2\n",
      "44 as\n",
      "45 of\n",
      "46 December\n",
      "47 13th\n",
      "48 ,\n",
      "49 2019\n",
      "50 .\n",
      "51 lol\n"
     ]
    }
   ],
   "source": [
    "# For each token, print its token number and token text \n",
    "i = 0\n",
    "for token in doc:\n",
    "    i += 1\n",
    "    print (f\"{i} {token.text}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stop Words\n",
    "Stop words are common words, which are often not needed for downstream tasks like word frequency analysis, topic modeling, count vectorizer (to reduce features for, say, bag-of-word text classifier), etc.  \n",
    "\n",
    "[Some AI researchers](https://twitter.com/deliprao/status/1068555626299584512) argue that stop word removal is detrimental for deep learning models as the removal can potentially change the meanings of documents (e.g. negation).\n",
    "\n",
    "Stop word list can be customized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By default, spaCy contains a few hundred stop words for English \n",
    "spacy_stop_words = spacy.lang.en.stop_words.STOP_WORDS\n",
    "len(spacy_stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['front',\n",
       " 'a',\n",
       " 'thru',\n",
       " 'someone',\n",
       " 'twelve',\n",
       " \"'ve\",\n",
       " 'also',\n",
       " 'therefore',\n",
       " 'however',\n",
       " 'doing',\n",
       " 'onto',\n",
       " 'us',\n",
       " 'seeming',\n",
       " 'down',\n",
       " 'more',\n",
       " 'two',\n",
       " 'full',\n",
       " 'whose',\n",
       " 'sixty',\n",
       " 'anyhow',\n",
       " 'eleven',\n",
       " 'might',\n",
       " 'somehow',\n",
       " 'hence',\n",
       " 'too',\n",
       " 'becoming',\n",
       " 'few',\n",
       " 'noone',\n",
       " 'ca',\n",
       " 'one']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print 30 stop words\n",
    "list(spacy_stop_words)[:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lemmatization  \n",
    "Lemmatization converts a word into its inflected form (lemma) while still ensuring that the reduced form (lemma) belongs to the language, which might not be the case for Stemming. See the table below for an example.  \n",
    "\n",
    "Lemmatization depends on part-of-speech (**POS**) tagging, and is rule-based (for now).  \n",
    "\n",
    "As with stop words, lemmatization can help downstream tasks by normalizing text, but for deep learning tasks, it is almost always skipped.  \n",
    "\n",
    "Stemming is not available in spaCy.  \n",
    "\n",
    "Word | Stem | Lemma\n",
    "--- | --- | ---\n",
    "Studies | Studi | Study\n",
    "Studying | Study | Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Hey \t\t hey\n",
      "2 , \t\t ,\n",
      "3 I \t\t -PRON-\n",
      "4 'm \t\t be\n",
      "5 Yuibi \t\t Yuibi\n",
      "6 . \t\t .\n",
      "7 I \t\t -PRON-\n",
      "8 work \t\t work\n",
      "9 very \t\t very\n",
      "10 hard \t\t hard\n",
      "11 at \t\t at\n",
      "12 XYZ \t\t XYZ\n",
      "13 , \t\t ,\n",
      "14 Inc. \t\t Inc.\n",
      "15 as \t\t as\n",
      "16 a \t\t a\n",
      "17 data \t\t data\n",
      "18 scientist \t\t scientist\n",
      "19 . \t\t .\n",
      "20 It \t\t -PRON-\n",
      "21 's \t\t be\n",
      "22 located \t\t locate\n",
      "23 in \t\t in\n",
      "24 San \t\t San\n",
      "25 Antonio \t\t Antonio\n",
      "26 , \t\t ,\n",
      "27 TX \t\t TX\n",
      "28 , \t\t ,\n",
      "29 which \t\t which\n",
      "30 is \t\t be\n",
      "31 the \t\t the\n",
      "32 best \t\t good\n",
      "33 city \t\t city\n",
      "34 ! \t\t !\n",
      "35 😊 \t\t 😊\n",
      "36 You \t\t -PRON-\n",
      "37 can \t\t can\n",
      "38 buy \t\t buy\n",
      "39 3 \t\t 3\n",
      "40 tacos \t\t taco\n",
      "41 for \t\t for\n",
      "42 $ \t\t $\n",
      "43 2 \t\t 2\n",
      "44 as \t\t as\n",
      "45 of \t\t of\n",
      "46 December \t\t December\n",
      "47 13th \t\t 13th\n",
      "48 , \t\t ,\n",
      "49 2019 \t\t 2019\n",
      "50 . \t\t .\n",
      "51 lol \t\t lol\n"
     ]
    }
   ],
   "source": [
    "# For each token, print its token number, token text, and lemma \n",
    "i = 0\n",
    "for token in doc:\n",
    "    i += 1\n",
    "    print (f\"{i} {token.text} \\t\\t {token.lemma_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentence Segmentation  \n",
    "SpaCy can split a document into individual sentences by predicting sentence boundaries. As with most features in spaCy, this can be customized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Hey, I'm Yuibi.\n",
      "2 I work very hard at XYZ, Inc. as a data scientist.\n",
      "3 It's located in San Antonio, TX, which is the best city!\n",
      "4 😊\n",
      "5 You can buy 3 tacos for $2 as of December 13th, 2019.\n",
      "6 lol\n"
     ]
    }
   ],
   "source": [
    "# for each sentence, print sentence number and text\n",
    "i = 0\n",
    "for sentence in doc.sents:\n",
    "    i += 1\n",
    "    print (f\"{i} {sentence}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Models  \n",
    "SpaCy comes with 3 deep neural network (DNN) based models:\n",
    "1. Part-of-speech (**POS**) tagger\n",
    "2. Named entity recognizer (**NER**)\n",
    "3. Syntactic dependency parser\n",
    "\n",
    "SpaCy used supervised Seq2Seq convolutional neural network (CNN) with sub-word embedding, residual connections, and layer normalization in a multi-task fashion to train those models. These models can be retrained if you have your own labeled data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part-of-speech (**POS**)  \n",
    "POS indicates which category a word is assigned to in accordance with its syntactic functions.  \n",
    "\n",
    "Examples:\n",
    "- Noun\n",
    "- Pronoun\n",
    "- Proper noun\n",
    "- Adjective\n",
    "- Verb\n",
    "- Adverb\n",
    "- Adposition\n",
    "- Auxiliary\n",
    "- Punctuation\n",
    "- Determiner \n",
    "- Subordinating conjunction\n",
    "- Interjection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Hey \t\t INTJ\n",
      "2 , \t\t PUNCT\n",
      "3 I \t\t PRON\n",
      "4 'm \t\t AUX\n",
      "5 Yuibi \t\t PROPN\n",
      "6 . \t\t PUNCT\n",
      "7 I \t\t PRON\n",
      "8 work \t\t VERB\n",
      "9 very \t\t ADV\n",
      "10 hard \t\t ADV\n",
      "11 at \t\t ADP\n",
      "12 XYZ \t\t PROPN\n",
      "13 , \t\t PUNCT\n",
      "14 Inc. \t\t PROPN\n",
      "15 as \t\t SCONJ\n",
      "16 a \t\t DET\n",
      "17 data \t\t NOUN\n",
      "18 scientist \t\t NOUN\n",
      "19 . \t\t PUNCT\n",
      "20 It \t\t PRON\n",
      "21 's \t\t AUX\n",
      "22 located \t\t VERB\n",
      "23 in \t\t ADP\n",
      "24 San \t\t PROPN\n",
      "25 Antonio \t\t PROPN\n",
      "26 , \t\t PUNCT\n",
      "27 TX \t\t PROPN\n",
      "28 , \t\t PUNCT\n",
      "29 which \t\t DET\n",
      "30 is \t\t AUX\n",
      "31 the \t\t DET\n",
      "32 best \t\t ADJ\n",
      "33 city \t\t NOUN\n",
      "34 ! \t\t PUNCT\n",
      "35 😊 \t\t X\n",
      "36 You \t\t PRON\n",
      "37 can \t\t VERB\n",
      "38 buy \t\t VERB\n",
      "39 3 \t\t NUM\n",
      "40 tacos \t\t NOUN\n",
      "41 for \t\t ADP\n",
      "42 $ \t\t SYM\n",
      "43 2 \t\t NUM\n",
      "44 as \t\t SCONJ\n",
      "45 of \t\t ADP\n",
      "46 December \t\t PROPN\n",
      "47 13th \t\t NOUN\n",
      "48 , \t\t PUNCT\n",
      "49 2019 \t\t NUM\n",
      "50 . \t\t PUNCT\n",
      "51 lol \t\t INTJ\n"
     ]
    }
   ],
   "source": [
    "# For each token, print its token number, token text, and POS \n",
    "i = 0\n",
    "for token in doc:\n",
    "    i += 1\n",
    "    print (f\"{i} {token.text} \\t\\t {token.pos_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Named Entity Recognizer (**NER**)  \n",
    "NER locates named entities and classifies them into pre-defined categories, such as:  \n",
    "- PERSON\tPeople, including fictional.\n",
    "- FAC\tBuildings, airports, highways, bridges, etc.\n",
    "- ORG\tCompanies, agencies, institutions, etc.\n",
    "- GPE\tCountries, cities, states.\n",
    "- PRODUCT\tObjects, vehicles, foods, etc. (Not services.)\n",
    "- EVENT\tNamed hurricanes, battles, wars, sports events, etc.\n",
    "- DATE\tAbsolute or relative dates or periods.\n",
    "- TIME\tTimes smaller than a day.\n",
    "- MONEY\tMonetary values, including unit.\n",
    "- QUANTITY\tMeasurements, as of weight or distance.\n",
    "- ORDINAL\t“first”, “second”, etc.\n",
    "- CARDINAL\tNumerals that do not fall under another type.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Yuibi \t\t PERSON\n",
      "2 XYZ, Inc. \t\t ORG\n",
      "3 San Antonio \t\t GPE\n",
      "4 TX \t\t GPE\n",
      "5 3 \t\t CARDINAL\n",
      "6 2 \t\t MONEY\n",
      "7 December 13th \t\t DATE\n",
      "8 2019 \t\t DATE\n"
     ]
    }
   ],
   "source": [
    "# For each extracted token, print its token number, token text, and named entities \n",
    "i = 0\n",
    "for token in doc.ents:\n",
    "    i += 1\n",
    "    print (f\"{i} {token.text} \\t\\t {token.label_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize NER\n",
    "spacy.displacy.render(doc, style='ent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Syntactic Dependency Parser  \n",
    "This process extracts the dependency parse of a sentence to represent its grammatical structure. The extracted structured is represented as directed graph, and it can be used as features in some deep learning algorithms (Tree-Recursive Neural Network, Graph Neural Network, etc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Hey \t\t intj \t\t 'm\n",
      "2 , \t\t punct \t\t 'm\n",
      "3 I \t\t nsubj \t\t 'm\n",
      "4 'm \t\t ROOT \t\t 'm\n",
      "5 Yuibi \t\t attr \t\t 'm\n",
      "6 . \t\t punct \t\t 'm\n",
      "7 I \t\t nsubj \t\t work\n",
      "8 work \t\t ROOT \t\t work\n",
      "9 very \t\t advmod \t\t hard\n",
      "10 hard \t\t advmod \t\t work\n",
      "11 at \t\t prep \t\t work\n",
      "12 XYZ \t\t pobj \t\t at\n",
      "13 , \t\t punct \t\t Inc.\n",
      "14 Inc. \t\t appos \t\t XYZ\n",
      "15 as \t\t prep \t\t work\n",
      "16 a \t\t det \t\t scientist\n",
      "17 data \t\t compound \t\t scientist\n",
      "18 scientist \t\t pobj \t\t as\n",
      "19 . \t\t punct \t\t work\n",
      "20 It \t\t nsubjpass \t\t located\n",
      "21 's \t\t auxpass \t\t located\n",
      "22 located \t\t ROOT \t\t located\n",
      "23 in \t\t prep \t\t located\n",
      "24 San \t\t compound \t\t Antonio\n",
      "25 Antonio \t\t pobj \t\t in\n",
      "26 , \t\t punct \t\t Antonio\n",
      "27 TX \t\t appos \t\t Antonio\n",
      "28 , \t\t punct \t\t Antonio\n",
      "29 which \t\t nsubj \t\t is\n",
      "30 is \t\t relcl \t\t Antonio\n",
      "31 the \t\t det \t\t city\n",
      "32 best \t\t amod \t\t city\n",
      "33 city \t\t attr \t\t is\n",
      "34 ! \t\t punct \t\t located\n",
      "35 😊 \t\t ROOT \t\t 😊\n",
      "36 You \t\t nsubj \t\t buy\n",
      "37 can \t\t aux \t\t buy\n",
      "38 buy \t\t ROOT \t\t buy\n",
      "39 3 \t\t nummod \t\t tacos\n",
      "40 tacos \t\t dobj \t\t buy\n",
      "41 for \t\t prep \t\t buy\n",
      "42 $ \t\t nmod \t\t 2\n",
      "43 2 \t\t pobj \t\t for\n",
      "44 as \t\t prep \t\t buy\n",
      "45 of \t\t prep \t\t as\n",
      "46 December \t\t compound \t\t 13th\n",
      "47 13th \t\t pobj \t\t of\n",
      "48 , \t\t punct \t\t 13th\n",
      "49 2019 \t\t npadvmod \t\t 13th\n",
      "50 . \t\t punct \t\t buy\n",
      "51 lol \t\t ROOT \t\t lol\n"
     ]
    }
   ],
   "source": [
    "# For each token, print its token number, token text, and dependency \n",
    "i = 0\n",
    "for token in doc:\n",
    "    i += 1\n",
    "    print (f\"{i} {token.text} \\t\\t {token.dep_} \\t\\t {token.head.text}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    <path class=\"displacy-arc\" id=\"arrow-69b9827f2d004adc873ccb39adf01a16-4-11\" stroke-width=\"2px\" d=\"M1995,352.0 C1995,264.5 2135.0,264.5 2135.0,352.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "        <textPath xlink:href=\"#arrow-69b9827f2d004adc873ccb39adf01a16-4-11\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">npadvmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M2135.0,354.0 L2143.0,342.0 2127.0,342.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "</svg>\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"en\" id=\"69b9827f2d004adc873ccb39adf01a16-5\" class=\"displacy\" width=\"225\" height=\"137.0\" direction=\"ltr\" style=\"max-width: none; height: 137.0px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"47.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">lol</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">INTJ</tspan>\n",
       "</text>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize syntactic dependency\n",
    "# Split into sentences\n",
    "sentence_spans = list(doc.sents)\n",
    "spacy.displacy.render(sentence_spans, style=\"dep\")"
   ]
  }
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